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i18n-ai-translate -- automated i18next translations with ChatGPT/Gemini

Hacker News

i18n-ai-translate -- automated i18next translations with ChatGPT/Gemini

Modern LLMs tend to be on par with machine translation and are only going to get better. By incrementally providing the i18n file as input, context-aware translations are generated instead of considering each key as a separate entity. Furthermore, running a separate verification flow on each generated translation ensures consistency in the output. Instead of copy-pasting translations from i18n files to ChatGPT, an arduous process when there are many supported languages, this library automatically generates the updated translations.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, gemini · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, context, chatgpt · Missing: agents, macos, agent
71%71% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
25%25% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
10%10% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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